Aviation Engine Special-shaped Coating Detection Method and System Based on MEMS-OCT Micro-Nano Probe

Abstract

The present invention discloses a method and system for detecting special-shaped coatings of aeroengines based on a MEMS-OCT micro-nano probe, which relates to the field of microelectromechanical systems. The method includes: through parametric surface modeling and adaptive path planning, for high-precision scanning of the micro-nano probe to cover the complex coating geometric surface; obtaining depth-resolved reflection signals of the internal structure of the coating through the interference principle, and converting them into frequency-domain information for reconstructing tomographic images; preprocessing the obtained depth-resolved reflection signals; reconstructing the three-dimensional coating structure through a preset algorithm to quantify the coating thickness distribution; segmenting the internal defect areas of the coating to quantify the defect morphology; and through extracting coating thickness and defect characteristics, performing risk assessment and health status classification. By combining the MEMS-OCT micro-nano probe with parametric surface modeling and adaptive path planning, high-precision scanning, three-dimensional imaging and precise quantification of defects of special-shaped coatings of aeroengines are realized, thereby greatly improving the accuracy, efficiency and reliability of coating detection.

Description

Technical Field

[0001] The present invention relates to the field of microelectromechanical systems, and specifically to a method and system for detecting special-shaped coatings of aeroengines based on a MEMS-OCT micro-nano probe. Background Art

[0002] With the continuous development of aeroengine technology, the high performance and high reliability requirements of aeroengines make the coating quality control in the manufacturing process particularly important. The coating is an important protective layer for many key components in aeroengines. It can not only improve the corrosion resistance, wear resistance, and oxidation resistance of the engine, but also effectively extend the service life of the engine. Therefore, the quality of the coating is directly related to the operating stability and safety of the engine.

[0003] However, the coatings of aeroengine components usually present complex special-shaped geometric structures. The coating thickness distribution, defect types, and their spatial distributions have a profound impact on the performance of the engine. Traditional coating detection methods, such as optical microscopes and scanning electron microscopes, often have difficulty meeting the high-precision detection requirements of special-shaped coatings. These methods usually can only obtain local information of the coating on a two-dimensional plane, and the operation is complex, time-consuming, and cannot achieve efficient and real-time detection.

[0004] Therefore, to solve the above problems, the present technology proposes a method for detecting special-shaped coatings of aeroengines based on a MEMS-OCT micro-nano probe, aiming to achieve high-precision scanning coverage of the complex coating geometric surface through parametric surface modeling and adaptive path planning; obtain depth-resolved reflection signals of the internal structure of the coating through the interference principle, and reconstruct tomographic images through frequency-domain information; use advanced signal processing and algorithms to reconstruct the three-dimensional structure of the coating, quantify the coating thickness distribution and defect morphology; finally, evaluate the risk of the coating and classify the health status by extracting the coating thickness and defect characteristics. This method can provide quantitative analysis of internal defects of the coating and high-precision health status assessment without damaging the coating, significantly improving the efficiency and accuracy of coating detection. Summary of the Invention

[0005] Based on the above-mentioned disadvantages of the prior art, the purpose of the present invention is to provide a method and system for detecting special-shaped coatings of aeroengines based on a MEMS-OCT micro-nano probe to solve the above technical problems.

[0006] To achieve the above purpose, the present invention provides the following technical solution: A method for detecting special-shaped coatings of aeroengines based on a MEMS-OCT micro-nano probe, comprising:

[0007] Through parametric surface modeling and adaptive path planning, for high-precision scanning coverage of the complex coating geometric surface by the micro-nano probe;

[0008] The depth-resolved reflection signal of the internal structure of the coating is obtained through the interference principle and converted into frequency-domain information for reconstructing the tomographic image;

[0009] Preprocess the obtained depth-resolved reflection signal;

[0010] Reconstruct the three-dimensional coating structure through a preset algorithm to quantify the coating thickness distribution;

[0011] Segment the internal defect area of the coating and quantify the defect morphology;

[0012] Through extracting the coating thickness and defect characteristics, conduct risk assessment and health status classification.

[0013] The present invention is further configured such that through parametric surface modeling and adaptive path planning, the complex coating geometric surface is scanned and covered, and the steps are as follows:

[0014] Parametric surface modeling, representing the coating surface as a parametric surface through NURBS surface , where is the normalized parameter used to describe the position of any point on the surface;

[0015] Adaptive path planning, extracting the local geometric feature Gaussian curvature of the surface through the parametric surface model , defining the objective function , through the weighted sum of the Gaussian curvature and the contact force , dynamically balance the scanning density of the probe for the curvature-sensitive area and the contact pressure control. The objective function formula is as follows: , where is the parameter space gradient, is the amplitude of the contact force between the probe and the surface, is the weight factor.

[0016] The present invention is further configured such that the depth-resolved reflection signal of the internal structure of the coating is obtained through the interference principle and converted into frequency-domain information for reconstructing the tomographic image, and the steps are as follows:

[0017] Model the interference signal. The OCT interference signal intensity is composed of the measurement parameters in the OCT system, and its formula is as follows: , where is the depth coordinate representing the distance from the sample surface to the detection point, represents the wave number of light, is the OCT interference signal intensity, represents the light source spectral density, is the reflection coefficient of the reference arm, is the surface volatility function of the sample, which describes the influence of minute surface fluctuations on the signal intensity. is the frequency-domain modulation function, which represents the influence of the modulation frequency on the signal intensity. is the depth position correction factor, which reflects the signal gain and attenuation at different depths. is the optical field modulation function, which controls the relationship between the change in the light source frequency and the sample reflection. is the interference intensity function of the reflected light;

[0018] Frequency-domain demodulation and signal extraction are performed on the obtained interference signal by performing a fast Fourier transform and suppressing noise and spectral leakage through a frequency-domain window function to reconstruct the depth-resolved reflection signal: , which is used to accurately reflect the internal structure of the coating.

[0019] The present invention is further configured such that the obtained depth-resolved reflection signal is preprocessed, and noise in the OCT signal is removed through adaptive wavelet threshold denoising and non-local means filtering to obtain preprocessed multi-angle OCT data .

[0020] The present invention is further configured such that the three-dimensional coating structure is reconstructed through a preset algorithm, the coating thickness distribution is quantified, and three-dimensional image reconstruction is performed according to the preprocessed multi-angle OCT data by introducing the interference field and reflectivity response parameters in spatial tomography for more accurate surface and internal structure restoration. The formula is: , where is the three-dimensional data of the reconstructed coating, representing the three-dimensional coating structure value at the spatial position , is the preprocessed multi-angle OCT data, representing the reflection intensity signal measured at the depth and the angle , is the radial position in the spatial coordinate, is the Dirac function, is the angle, representing the scanning direction of the probe. The formula for reconstructing the three-dimensional data of the coating constructs the three-dimensional structure of the coating through the combination of projection and backprojection;

[0021] By integrating the reflectivity along the normal direction, the thickness distribution of the coating is calculated. The formula is: , where is the substrate position and is the surface position.

[0022] The present invention is further configured such that the internal defect area of the segmented coating is quantified in terms of defect morphology, and the steps are as follows:

[0023] Construct an energy function , and segment the defect boundary through a minimization process. The energy function formula is:

[0024] , where is the total energy function, representing the edge detection energy of the coating, represents the change direction and amplitude of the coating gray value, is the gray value of the coating, is the background intensity, is the weight coefficient for controlling the gradient term, is the weight coefficient for controlling the brightness difference term, is the weight coefficient of the defect indicator function, is the defect indicator function, indicating whether the area in the coating belongs to the defect area, is the coating domain;

[0025] According to the energy function formula, solve the minimum value of the energy function. When the energy change rate reaches the threshold, terminate the iteration, output the segmentation result, and convert the segmentation result into a binary defect mask , to distinguish defects from the background;

[0026] Eliminate the noise and holes in the binary defect mask , and repair the defect area. The formula is: , where is the repaired defect mask. The formula adopts a combination operation of dilation followed by erosion. The dilation operation expands the defect area and fills small holes. The dilation operation formula is , where is the dilation operation on the pixel position , is the structuring element relative to the pixel position offset, is each element inside the structuring element. The erosion operation shrinks the area and removes isolated noise points. The erosion operation formula is , where is the erosion operation on the pixel position , is the size of the spherical structuring element.

[0027] The present invention is further configured such that by extracting the coating thickness and defect features, risk assessment and health status classification are performed, and the features include the difference degree of the coating defect profile, the local defect concentration. The difference degree of the coating defect profile measures the morphological difference of the coating defect, and the local defect concentration measures the density of defects in a certain local area. The quality of the coating is reflected by the spatial structure of the defect distribution.

[0028] The present invention is further configured such that for the difference degree of the coating defect profile, its calculation formula is: , where is the difference degree of the coating defect profile, is the reflectivity of the coating at point , is the reflectivity of the ideal defect model, is the reference position on the coating surface, is the scale parameter, which controls the distance weight between the defect profile and the ideal model;

[0029] For the local defect concentration, its calculation formula is: , where is the local defect concentration, is the local area of the coating, is the preset threshold of the coating reflectivity to determine the presence or absence of defects, is the scale factor that controls the turning of the control function.

[0030] The present invention is further configured such that according to the extracted features, a feature vector is constructed, and a support vector machine is used to classify the features, and the risk is evaluated based on the state of the coating.

[0031] The present invention also provides an aircraft engine special-shaped coating detection system based on a MEMS-OCT micro-nano probe. The system includes:

[0032] Coating scanning module: Through parametric surface modeling and adaptive path planning, it is used for high-precision scanning of the micro-nano probe to cover the complex coating geometric surface;

[0033] Signal acquisition module: Through the interference principle, the depth-resolved reflection signal of the coating internal structure is obtained and converted into frequency-domain information for reconstructing the tomographic image;

[0034] Signal preprocessing module: Preprocesses the obtained depth-resolved reflection signal;

[0035] Quantification of coating thickness distribution module: Reconstructs the three-dimensional coating structure through a preset algorithm to quantify the coating thickness distribution;

[0036] Quantification of defect morphology module: Segments the internal defect area of the coating to quantify the defect morphology;

[0037] Feature extraction and risk assessment module: Perform risk assessment and health status classification by extracting coating thickness and defect features.

[0038] The present invention provides a method and system for detecting special-shaped coatings of aircraft engines based on MEMS-OCT micro-nano probes. The method uses parameterized surface modeling and adaptive path planning to allow micro-nano probes to scan complex coating geometric surfaces with high precision; obtains depth-resolved reflection signals of the internal structure of the coating by interference principle, and converts them into frequency domain information for reconstructing tomographic images; pre-processes the obtained depth-resolved reflection signals; reconstructs the three-dimensional coating structure by a preset algorithm, and quantifies the coating thickness distribution; segments the internal defect area of ​​the coating, and quantifies the defect morphology; and extracts the coating thickness and defect characteristics to perform risk assessment and health status classification. The beneficial effects produced include:

[0039] High-precision coating scanning and full coverage: Through parametric surface modeling and adaptive path planning, the scanning problem of complex coating geometric surfaces is effectively solved. The MEMS-OCT micro-nano probe can accurately scan and cover the special-shaped coating surface of the aircraft engine, thereby achieving comprehensive and efficient detection of the entire coating surface, avoiding the problem of difficulty in scanning complex parts of the coating in traditional methods;

[0040] High-resolution three-dimensional imaging and acquisition of depth-resolved reflection signals: The depth-resolved reflection signals are acquired through the interference principle and converted into frequency domain information for reconstruction, which can accurately reconstruct the three-dimensional image of the internal structure of the coating. This process greatly improves the accuracy of coating defect identification, especially in coating thickness distribution and the location of tiny defects inside the coating, which is superior to traditional two-dimensional detection methods;

[0041] Accurate quantification of coating thickness and defect morphology: Advanced algorithms are introduced to quantify coating thickness, and by segmenting and identifying defect areas inside the coating, defect morphology and spatial distribution information can be accurately extracted. This precise quantification and defect classification provides high-quality input for subsequent risk assessment and health status classification, and can fully reflect the quality status of the coating.

[0042] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings. In the accompanying drawings:

[0044] Figure 1 The flowchart of the method for detecting special-shaped coatings of aero engines based on the MEMS-OCT micro-nano probe shown in an exemplary embodiment of the present invention;

[0045] Figure 2 The structural schematic diagram of the system for detecting special-shaped coatings of aero engines based on the MEMS-OCT micro-nano probe shown in an exemplary embodiment of the present invention. Detailed implementation manners

[0046] The following will illustrate the implementation manners of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention, rather than for limiting the protection scope of the present invention.

[0047] It should be noted that the illustrations provided in the following embodiments only schematically illustrate the basic concept of the present invention. Therefore, only the components related to the present invention are shown in the drawings, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, number, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0048] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand. Embodiment 1

[0049] The method for detecting special-shaped coatings of aero engines based on the MEMS-OCT micro-nano probe, as Figure 1 shown, includes:

[0050] Through parametric surface modeling and adaptive path planning, it is used for the high-precision scanning of the micro-nano probe to cover the complex coating geometric surface;

[0051] Obtain the depth-resolved reflection signal of the internal structure of the coating through the interference principle, and convert it into frequency-domain information for reconstructing the tomographic image;

[0052] Preprocess the obtained depth-resolved reflection signal;

[0053] Reconstruct the three-dimensional coating structure through a preset algorithm to quantify the coating thickness distribution;

[0054] Segment the internal defect area of the coating and quantify the defect morphology;

[0055] Extract the coating thickness and defect characteristics to conduct risk assessment and health status classification.

[0056] The present invention is further configured such that through parametric surface modeling and adaptive path planning, the complex coating geometric surface is scanned and covered, and the steps are as follows:

[0057] Parametric surface modeling, representing the coating surface as a parametric surface through NURBS surface , where is the normalized parameter. Each point on the coating surface can be described by a two-dimensional parameter space, and this parameter space is normalized with a value range of , ensuring that each part of the surface can be scanned in detail. Through parametric surface modeling, the geometric characteristics of the coating can be accurately described, ensuring a reasonable scanning density of the probe in each area, especially in areas with large curvature changes;

[0058] Adaptive path planning, extracting the local geometric feature Gaussian curvature of the surface through the parametric surface model , where the Gaussian curvature is a scalar representing the degree of surface curvature, and its value at each point describes the bending situation of the surface at that point. For areas with large curvature, the probe will perform more intensive scanning. Define the objective function , through the weighted sum of the Gaussian curvature and the contact force , dynamically balance the scanning density of the probe in the curvature-sensitive area and the contact pressure control. The objective function formula is as follows: , where is the parameter space gradient, that is, the rate of change of the surface in the direction, calculated by the partial derivative of the parametric surface , is the amplitude of the contact force between the probe and the surface, representing the pressure magnitude when the probe contacts the coating surface during scanning, is the weight factor, controlling the relative importance ratio of the Gaussian curvature and the contact force in the objective function. The adaptive path planning can dynamically adjust the scanning density according to the geometric changes of the coating surface, increasing the scanning frequency in the curved area and decreasing the scanning frequency in the flat area, optimizing the scanning efficiency and accuracy. By considering the dynamic balance of the contact force and the curvature, it can avoid damaging the coating surface due to excessive pressure, and at the same time ensure sufficient scanning density in the area with a large curvature, improving the detection ability of coating defects.

[0059] The present invention is further configured such that the depth-resolved reflection signal of the internal structure of the coating is obtained by the interference principle and converted into frequency-domain information for reconstructing the tomographic image. The steps are as follows:

[0060] Model the interference signal, the OCT interference signal intensity is composed of the measurement parameters in the OCT system, reflecting the reflection and interference of light when passing through the sample. The formula is as follows: , where is the depth coordinate, representing the distance from the sample surface to the detection point, with a value range from a few micrometers to a few millimeters, specifically depending on the scanning depth and resolution. represents the wave number of light, in to rad / m, depending on the wavelength of the light source used. is the OCT interference signal intensity. represents the light source spectral density, describing the spectral distribution of the light waves emitted by the light source. is the reflection coefficient of the reference arm, describing the reflection intensity of the light in the reference arm. is the surface fluctuation function of the sample, describing the influence of surface micro-fluctuations on the signal intensity. is the frequency-domain modulation function, representing the influence of the modulation frequency on the signal intensity. is the depth position correction factor, reflecting the signal gain and attenuation at different depths, with a value range from 0 to 1. is the light field modulation function, controlling the relationship between the change of the light source frequency and the sample reflection. is the interference intensity function of the reflected light, representing the interference intensity between the sample reflected light and the reference light.

[0061] Frequency-domain demodulation and signal extraction. Perform a fast Fourier transform on the obtained interference signal to convert the time-domain signal into a frequency-domain signal, and suppress noise and spectral leakage through the frequency-domain window function to reconstruct the depth-resolved reflection signal: , which is used to accurately reflect the internal structure of the coating. By modeling the interference signal and using frequency-domain demodulation technology, the depth resolution of OCT imaging is improved, so as to accurately detect the internal structure of the coating. Through the reconstructed depth-resolved reflection signal, the internal structure changes of the coating can be accurately reflected, especially having significant advantages in the positioning and identification of micro-defects.

[0062] The present invention is further configured such that the obtained depth-resolved reflection signal is preprocessed, and the noise in the OCT signal is removed by adaptive wavelet threshold denoising and non-local mean filtering to obtain preprocessed multi-angle OCT data. , improving the image quality. Specifically, for the frequency-domain signal is subjected to discrete wavelet transform and decomposed into multi-scale coefficients. . The noise is suppressed by a non-linear threshold function and the signal is reconstructed. After that, based on the similarity weight the neighboring pixels are weighted and averaged to obtain .

[0063] The present invention is further configured such that the three-dimensional coating structure is reconstructed by a preset algorithm, and the coating thickness distribution is quantified. According to the preprocessed multi-angle OCT data a three-dimensional image reconstruction is performed, and the interference field and reflectivity response parameters in spatial tomography are introduced for more accurate surface and internal structure restoration. The formula is: , where is the three-dimensional data of the reconstructed coating, representing the coating intensity information of each point in the three-dimensional space. is the preprocessed multi-angle OCT data, representing the reflection intensity signal measured at the depth and the angle . is the angle, representing the scanning direction of the probe. This formula constructs the three-dimensional structure of the coating through the combination of projection and back-projection. is used to achieve spatial projection. is the Dirac function, which is used to describe having an infinite value at a certain point and being zero elsewhere, helping to project the reflection signal into the three-dimensional space according to its spatial coordinates. is the radial position in the spatial coordinates, representing the distance in the plane. By combining multi-angle data for reconstruction and calculation, it can handle complex coating geometries and provide comprehensive coating quality information.

[0064] By integrating the reflectivity along the normal direction, the thickness distribution of the coating is calculated. The formula is: , where is the substrate position and is the surface position, indicating the reflection intensity of the coating at the position By using the integral method of the reflectivity along the normal direction, the thickness distribution of the coating can be accurately quantified, especially the change in the coating thickness at different positions, which is crucial for coating quality control.

[0065] The present invention is further configured such that the internal defect region of the segmented coating is quantified for the defect morphology, and the steps are as follows:

[0066] Construct an energy function , which combines the gradient of the coating gray value, the gray difference, and the indication information of the defect region. The defect boundary is segmented through the minimization process, and the energy function formula is: , where is the total energy function, representing the edge detection energy of the coating, represents the change direction and amplitude of the coating gray value. By calculating the change in the gray value, the edges in the coating can be detected. is the gray value of the coating, representing the brightness of each pixel in the image. is the background intensity, representing the gray value of the external region of the coating, which is used to compare with the gray value of the coating itself. is the weight coefficient for controlling the gradient term, is the weight coefficient for controlling the brightness difference term, is the weight coefficient of the defect indication function, controlling the indication intensity of the defect region. is the defect indication function, indicating whether each pixel in the coating belongs to the defect region. The defect region is 1, and other regions are 0. is the coating domain;

[0067] According to the energy function formula, solve the minimum value of the energy function to obtain the defect boundary in the coating. When the energy change rate reaches the threshold, terminate the iteration and output the segmentation result, and convert the segmentation result into a binary defect mask , where the defect region is 1 and other regions are 0, to distinguish the defect from the background;

[0068] Eliminate the noise and holes in the binary defect mask , and repair the defect region. The formula is: , where is the repaired defect mask. The formula adopts a combination operation of dilation followed by erosion. Through the combination operation of dilation and erosion, the noise and holes in the binary defect mask can be repaired to obtain a smoother and more continuous defect region. The dilation operation expands the defect region and fills small holes. The dilation operation formula is , where is the pixel position The dilation operation is the structuring element relative to the pixel position offset, For each element inside the structuring element, the erosion operation shrinks the area and removes isolated noise points. The formula is , where is the erosion operation on the pixel position , is the size of the spherical structuring element, which is used to control the operation size of dilation and erosion. Through precise segmentation and repair, the defect morphology in the coating, such as cracks and pores, can be better identified, thus providing accurate data support for subsequent analysis and evaluation.

[0069] The present invention is further configured such that, by extracting the coating thickness and defect features, risk assessment and health status classification are performed. The features include the coating defect profile difference degree and the local defect concentration. The coating defect profile difference degree measures the morphological difference of the coating defect, and the local defect concentration measures the density of defects in a certain local area. The quality of the coating is reflected by the spatial structure of the defect distribution.

[0070] The present invention is further configured such that the formula for calculating the coating defect profile difference degree is as follows: , where is the coating defect profile difference degree, is the reflectivity of the coating at point , is the reflectivity of the ideal defect model, representing the reflection intensity in the ideal case at this position. Based on the assumed defect-free coating model, is the reference position on the coating surface, is the scale parameter, which controls the distance weight between the defect profile and the ideal model. The coating defect profile difference degree is used to measure the difference between a defect at a certain position in the coating and the ideal defect model, is used to weight the defect depth. The closer the defect is to the surface, the greater the difference weight;

[0071] The formula for calculating the local defect concentration is as follows: , where is the local defect concentration, is the local area of the coating, is the preset threshold of the coating reflectivity to determine the presence or absence of defects.

[0072] If , it is considered that there is a defect at this position, is the scale factor that controls the turning of the control function. The local defect concentration is used to measure the local defect concentration at a certain position in the coating, based on the reflectivity For the distribution, the sigmoid function is used to classify the presence of defects. The local defect concentration combines the reflectivity with a preset threshold to control the sensitivity of defect judgment;

[0073] By calculating the defect profile difference and the local defect concentration, the type, morphology, and severity of defects in the coating can be accurately evaluated. By comparing the reflectivity with the ideal model, minute defects in the coating can be effectively identified, and their concentration and depth can be accurately evaluated.

[0074] The present invention is further configured such that, based on the extracted features, a feature vector is constructed, and a support vector machine is used to classify the features, and the risk is evaluated based on the state of the coating. Embodiment 2

[0075] Please refer to Figure 2 , the exemplary aero-engine special-shaped coating detection system based on the MEMS-OCT micro-nano probe includes:

[0076] Coating scanning module: Through parametric surface modeling and adaptive path planning, it is used for high-precision scanning of the micro-nano probe to cover the complex coating geometric surface;

[0077] Signal acquisition module: Through the interference principle, the depth-resolved reflection signal of the internal structure of the coating is obtained and converted into frequency-domain information for reconstructing the tomographic image;

[0078] Signal preprocessing module: Preprocesses the obtained depth-resolved reflection signal;

[0079] Quantification of coating thickness distribution module: Reconstructs the three-dimensional coating structure through a preset algorithm to quantify the coating thickness distribution;

[0080] Quantification of defect morphology module: Segments the internal defect area of the coating to quantify the defect morphology;

[0081] Feature extraction and risk assessment module: Through extracting the coating thickness and defect features, risk assessment and health status classification are performed.

[0082] It should be noted that the aero-engine special-shaped coating detection system based on the MEMS-OCT micro-nano probe provided in the above embodiments and the aero-engine special-shaped coating detection method based on the MEMS-OCT micro-nano probe provided in the above embodiments belong to the same concept. The specific ways in which each module and unit perform operations have been described in detail in the method embodiments, and will not be elaborated here. In practical applications, the aero-engine special-shaped coating detection system based on the MEMS-OCT micro-nano probe provided in the above embodiments can, according to needs, allocate the above functions to different functional modules, that is, divide the internal structure of the system into different functional modules to complete all or part of the functions described above. This will not be limited here either.

[0083] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0084] It should be understood that the term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. The specific meaning can be understood by referring to the context before and after.

[0085] In this application, "at least one" means one or more, and "a plurality of" means two or more. "At least one of the following" or a similar expression means any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0086] It should be understood that in various embodiments of this application, the magnitudes of the sequence numbers of the above - mentioned processes do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of this application.

[0087] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of this application.

[0088] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0089] In several embodiments provided in this application, it should be understood that the disclosed system can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0090] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] In addition, in each embodiment of the present application, each functional unit may be integrated in one processing unit, may exist physically alone for each unit, or two or more units may be integrated in one unit.

[0092] If the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it may be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or a part of the technical solution, may be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0093] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A detection method for special-shaped coatings of aero-engines based on a MEMS-OCT micro-nano probe, characterized in that, Including: Through parametric surface modeling and adaptive path planning, it is used for high-precision scanning coverage of complex coating geometric surfaces by a micro-nano probe. The steps are as follows: Parametric surface modeling, representing the coating surface as a parametric surface through NURBS surfaces , where is the normalized parameter used to describe the position of any point on the surface; Adaptive path planning, through a parametric surface model, extracts the local geometric feature Gaussian curvature of the surface , defines the objective function , through the Gaussian curvature and the contact force The weighted sum of, dynamically balances the scanning density of the probe in the curvature-sensitive area and the contact pressure control. The objective function formula is as follows: , where is the parameter space gradient, is the contact force amplitude between the probe and the surface, is the weight factor; Obtaining the depth-resolved reflection signal of the internal structure of the coating through the interference principle, and converting it into frequency-domain information for reconstructing the tomographic image; Preprocessing the obtained depth-resolved reflection signal; Reconstructing the three-dimensional coating structure through a preset algorithm to quantify the coating thickness distribution; Segmenting the internal defect area of the coating and quantifying the defect morphology; Performing risk assessment and health status classification by extracting the coating thickness and defect characteristics.

2. The method for detecting special-shaped coatings of aero engines based on an MEMS-OCT micro-nano probe according to claim 1, wherein, Obtaining the depth-resolved reflection signal of the internal structure of the coating through the interference principle, and converting it into frequency-domain information for reconstructing the tomographic image. The steps are as follows: Model the interference signal, the intensity of the OCT interference signal is composed of the measurement parameters in the OCT system, and its formula is as follows: , where is the depth coordinate, representing the distance from the sample surface to the detection point, represents the wave number of light, is the intensity of the OCT interference signal, represents the spectral density of the light source, is the reflection coefficient of the reference arm, is the surface fluctuation function of the sample, describing the influence of surface micro-fluctuations on the signal intensity, is the frequency-domain modulation function, representing the influence of the modulation frequency on the signal intensity, is the depth position correction factor, reflecting the signal gain and attenuation at different depths, is the optical field modulation function, controlling the relationship between the change of the light source frequency and the sample reflection, is the interference intensity function of the reflected light; Frequency-domain demodulation and signal extraction are performed on the obtained interference signal to perform a fast Fourier transform, and noise and spectral leakage are suppressed through a frequency-domain window function to reconstruct the depth-resolved reflection signal: , which is used to accurately reflect the internal structure of the coating.

3. The method for detecting special-shaped coatings of aero engines based on the MEMS-OCT micro-nano probe according to claim 2, wherein Preprocess the acquired depth-resolved reflection signal, remove the noise in the OCT signal through adaptive wavelet threshold denoising and non-local mean filtering, and obtain the preprocessed multi-angle OCT data .

4. The method for detecting special-shaped coatings of aero-engines based on an MEMS-OCT micro-nano probe according to claim 3, wherein Reconstruct the three-dimensional coating structure through a preset algorithm, quantify the coating thickness distribution, and based on the preprocessed multi-angle OCT data Perform three-dimensional image reconstruction, introduce the interference field and reflectivity response parameters in spatial tomography for more accurate surface and internal structure restoration. The formula is as follows: , where is the three-dimensional data of the reconstructed coating, representing the three-dimensional structure value of the coating at the spatial position , is the multi-angle OCT data after preprocessing, representing the reflection intensity signal measured at the depth and the angle , is the radial position in the spatial coordinate, is the Dirac function, is the angle, representing the scanning direction of the probe, Construct the three-dimensional structure of the coating through the combination of projection and back-projection; By integrating the reflectivity in the normal direction, the thickness of the coating is calculated distribution, and its formula is: , where is the substrate position and is the surface position.

5. The method for detecting special-shaped coatings of aero-engines based on a MEMS-OCT micro-nano probe according to claim 1, characterized in that Segmenting the internal defect area of the coating and quantifying the defect morphology. The steps are as follows: Construct an energy function , and the defect boundary is segmented through a minimization process. The energy function formula is as follows: , where is the total energy function, representing the edge detection energy of the coating, represents the change direction and amplitude of the coating gray value, is the gray value of the coating, is the background intensity, is the weight coefficient for controlling the gradient term, is the weight coefficient for controlling the brightness difference term, is the weight coefficient of the defect indicator function, is the defect indicator function, indicating whether the area in the coating belongs to the defect area, is the coating domain; According to the energy function formula, solve the minimum value of the energy function, terminate the iteration when the energy change rate reaches the threshold, output the segmentation result, and convert the segmentation result into a binary defect mask , to distinguish defects from the background; Eliminate the noise and holes in the binary defect mask, and repair the defect area. The formula is as follows: Among them, , where is the repaired defect mask. The formula uses a combination operation of dilation followed by erosion. The dilation operation expands the defect area and fills small holes. The dilation operation formula is , where is the dilation operation on the pixel position , is the structural element relative to the pixel position offset, is each element inside the structural element. The erosion operation shrinks the area and removes isolated noise points. The erosion operation formula is , where is the erosion operation on the pixel position , is the size of the spherical structural element.

6. The method for detecting special-shaped coatings of aero-engines based on a MEMS-OCT micro-nano probe according to claim 4 or 5, characterized in that Performing risk assessment and health status classification by extracting the coating thickness and defect characteristics. The characteristics include the difference degree of the coating defect profile, the local defect concentration. The difference degree of the coating defect profile measures the morphological difference of the coating defect, and the local defect concentration measures the density of defects in a certain local area. The quality of the coating is reflected by the spatial structure of the defect distribution.

7. The method for detecting special-shaped coatings of aero-engines based on a MEMS-OCT micro-nano probe according to claim 6, wherein Coating defect profile difference degree, and its calculation formula is: , where is the coating defect profile difference degree, is the reflectivity of the coating at point , is the reflectivity of the ideal defect model, is the reference position on the coating surface, is the scale parameter, controlling the distance weight between the defect profile and the ideal model; Local defect concentration, and its calculation formula is: , where is the local defect concentration, is the local area of the coating, is the preset threshold of the coating reflectivity to determine the presence or absence of defects, is the scale factor for controlling the turning of the function.

8. The method for detecting the special-shaped coating of an aero-engine based on the MEMS-OCT micro-nano probe according to claim 7, characterized in that Constructing a feature vector according to the extracted features, classifying the features using a support vector machine, and assessing the risk based on the state of the coating.

9. An aeroengine special-shaped coating detection system based on a MEMS-OCT micro-nano probe, which is used to implement the aeroengine special-shaped coating detection method based on the MEMS-OCT micro-nano probe according to any one of claims 1-8, is characterized in that Including: Coating scanning module: Used for high-precision scanning of the complex coating geometric surface by the micro-nano probe through parametric surface modeling and adaptive path planning; Signal acquisition module: Obtaining the depth-resolved reflection signal of the internal structure of the coating through the interference principle, and converting it into frequency-domain information for reconstructing the tomographic image; Signal preprocessing module: Preprocessing the obtained depth-resolved reflection signal; Quantifying coating thickness distribution module: Reconstructing the three-dimensional coating structure through a preset algorithm to quantify the coating thickness distribution; Quantifying defect morphology module: Segmenting the internal defect area of the coating and quantifying the defect morphology; Feature extraction and risk assessment module: Performing risk assessment and health status classification by extracting the coating thickness and defect characteristics.

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